Invited Speakers

James Wayman, San Jose State University

Title: Biometric Testing

Abstract: Large-scale biometric testing has a history of at least 25 years.   However, each test  has used different testing and reporting protocols, making results very hard to understand and causing longitudinal comparison of biometric device and system performance to be impossible.  Working Group 5 of the ISO/IEC JTC1 Standing Committee 37 on biometrics has been established to address these variances in the hope that a single test and reporting standard  can be developed.  But many within the field feel that a single "standard" might not be possible.  In this talk, we will review historical testing and reporting protocols, point out the areas of controversy, and analyze in detail the recent UK contribution to the SC37 WG5 process.

Josef Bigun,  Halmstad University

Title: Multimodal Biometric Authentication for Mobile Communication

Abstract: The elements of multi-modal authentication along with system models are presented. These include the machine experts as well as machine supervisors. These will be contrasted to human performance. In particular fingerprint and speech based systems will serve as illustrations of a mobile authentication application. A signal adaptive supervisor, based on the input biometric signal quality, will be discussed. Experimental results on data collected from mobile telephones are reported demonstrating the benefits of the proposed scheme in mobile communication systems. The presentation is based on these studies, for which the research documented in has been instrumental.

Gary Strong, U.S. Department of Homeland Security

Title: Current Issues in Biometrics at the Department of Homeland Security

Invited Panel Session

Organizer: Jonathon Phillips, NIST/DARPA

Panelists: Paul Griffin, Kevin Bowyer, Doug Reynolds

Title: Multi-Biometrics, Déjà vu?

Abstract: Multi-biometrics is currently a hot area of research in biometrics, pattern recognition, and computer vision. Multi-biometrics is the study of combining results from multiple biometric samples and multiple biometric algorithms. This includes (1) combining multiple modalities; e.g., face and fingerprints; (2) multiple samples of the same modality; e.g., five face images of the same person; and (3) multiple algorithms on the same sample; e.g., fusing the results of three speaker recognition algorithms on the same speech sample. All of these different approaches show improvement in performance. Looking at all these results gives the author a deja-vu feeling from face recognition in the 1990s. In the 1990s most pattern recognition techniques demonstrated a reasonable ability to recognition faces. It took a number of years to identify the most promising approaches.

The key question for multi-biometrics is how to determine the superior multi-biometrics approaches. Sorting out the merits of different multi-biometrics is more complex than with a single biometric modality. The first step in identifying a superior approach is to separate out the effects of the modality, algorithms for the individual modalities, and the fusion algorithm. Separating out these effects is necessary so that one does not condemn a modality or combination of modalities because of poor algorithms. This can be either a fusion algorithm or individual modality algorithm. A second level of analysis considers the scenario for using a multi-biometric system. Under different circumstances, different multi-biometric configurations will be preferable. From a cost perspective, a multiple sample approach will be most likely be cheaper than a multiple algorithm approach which is likely to be cheaper than a multiple modal approach. For robustness, a multiple modal approach is most likely to be best. To move forward in multi-biometrics, these issues and others need to be addressed. This panel will discuss these issues and raise others associated with assessing the state of multi-biometrics, and paths for advancing multi-biometrics.